Ketogenesis mitigates metabolic dysfunction–associated steatotic liver disease through mechanisms that extend beyond fat oxidation
Eric D. Queathem, … , Patrycja Puchalska, Peter A. Crawford
Eric D. Queathem, … , Patrycja Puchalska, Peter A. Crawford
Published April 24, 2025
Citation Information: J Clin Invest. 2025;135(12):e191021. https://doi.org/10.1172/JCI191021.
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Research Article Hepatology Metabolism

Ketogenesis mitigates metabolic dysfunction–associated steatotic liver disease through mechanisms that extend beyond fat oxidation

Abstract

The progression of metabolic dysfunction–associated steatotic liver disease (MASLD) to metabolic dysfunction–associated steatohepatitis (MASH) involves alterations in both liver-autonomous and systemic metabolism that influence the liver’s balance of fat accretion and disposal. Here, we quantify the contributions of hepatic oxidative pathways to liver injury in MASLD-MASH. Using NMR spectroscopy, UHPLC-MS, and GC-MS, we performed stable isotope tracing and formal flux modeling to quantify hepatic oxidative fluxes in humans across the spectrum of MASLD-MASH, and in mouse models of impaired ketogenesis. In humans with MASH, liver injury correlated positively with ketogenesis and total fat oxidation, but not with turnover of the tricarboxylic acid cycle. Loss-of-function mouse models demonstrated that disruption of mitochondrial HMG-CoA synthase (HMGCS2), the rate-limiting step of ketogenesis, impairs overall hepatic fat oxidation and induces an MASLD-MASH–like phenotype. Disruption of mitochondrial β-hydroxybutyrate dehydrogenase (BDH1), the terminal step of ketogenesis, also impaired fat oxidation, but surprisingly did not exacerbate steatotic liver injury. Taken together, these findings suggest that quantifiable variations in overall hepatic fat oxidation may not be a primary determinant of MASLD-to-MASH progression, but rather that maintenance of ketogenesis could serve a protective role through additional mechanisms that extend beyond overall rates of fat oxidation.

Authors

Eric D. Queathem, David B. Stagg, Alisa B. Nelson, Alec B. Chaves, Scott B. Crown, Kyle Fulghum, D. Andre d’Avignon, Justin R. Ryder, Patrick J. Bolan, Abdirahman Hayir, Jacob R. Gillingham, Shannon Jannatpour, Ferrol I. Rome, Ashley S. Williams, Deborah M. Muoio, Sayeed Ikramuddin, Curtis C. Hughey, Patrycja Puchalska, Peter A. Crawford

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Figure 2

Liver injury does not correlate with endogenous glucose production (EGP) or TCA cycle turnover.

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Liver injury does not correlate with endogenous glucose production (EGP)...
Fluxes through hepatic intermediary metabolic pathways were quantified in humans after oral administration of heavy water (2H2O) and [U-13C3]propionate. [3,4-13C2]Glucose and d-[U-13C4]βOHB were intravenously infused, allowing whole-body glucose (VEGP) and βOHB turnover (VRaβOHB) to be quantified at metabolic and isotopic steady state. (A) Fractional sourcing of glucose can be quantified from the 2H enrichment pattern of plasma glucose using 2H NMR, which, by multiplying by VEGP, allows absolute reaction velocities (V) (i.e., flux) for hepatic glucose sourcing pathways to be quantified. (B) Administration of [U-13C3]propionate 13C-enriches TCA cycle intermediates, which sources phosphoenolpyruvate (PEP). Using 13C NMR, and the multiplet arising from the C2 resonance of plasma glucose, the resulting metabolic network models oxidative and anaplerotic nodes of the TCA cycle in parallel to glucose production. By normalizing of fluxes to VPEP, the absolute reaction velocities of the TCA cycle, anaplerosis/cataplerosis, and pyruvate cycling can be quantified. (C) Average percentage of VEGP derived from glycogen, glycerol, and PEP, highlighting that TCA cycle–sourced PEP is the major contributor to VEGP in the fasted state in humans. (D) Average reducing equivalents (REs) derived from GNG, β-oxidation, and the TCA cycle. (E and F) The correlations of NAS with VEGP (E) and TCA cycle turnover (VCS) (F). Data are either expressed as mean ± SD or shown as correlations. Pearson’s correlation coefficients (r) are shown on each group along with a line of best fit and 95% confidence intervals calculated using linear regression. Correlations were accepted as significant if P < 0.05. P values are shown on each graph.